Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 316-325 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 60-61 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
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Classifying machinery condition using oil samples and binary logistic regression. / Phillips, J.; Cripps, Edward; Lau, John; Hodkiewicz, Melinda.
In: Mechanical Systems and Signal Processing, Vol. 60-61, No. 1, 2015, p. 316-325.Research output: Contribution to journal › Article
TY - JOUR
T1 - Classifying machinery condition using oil samples and binary logistic regression
AU - Phillips, J.
AU - Cripps, Edward
AU - Lau, John
AU - Hodkiewicz, Melinda
PY - 2015
Y1 - 2015
N2 - © 2015 Elsevier Ltd. All rights reserved. The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.
AB - © 2015 Elsevier Ltd. All rights reserved. The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.
U2 - 10.1016/j.ymssp.2014.12.020
DO - 10.1016/j.ymssp.2014.12.020
M3 - Article
VL - 60-61
SP - 316
EP - 325
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
IS - 1
ER -